A real estate report generator and property organizer that is powered ...

...by AI

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Swamp

The market has seen several mediocre solutions that nobody loves. Unless you can offer something fundamentally different, you’ll likely struggle to stand out or make money.

Should You Build It?

Don't build it.


Your are here

You're entering a crowded space with your AI-powered real estate report generator and property organizer. The "Swamp" category isn't encouraging, and with 21 similar products already out there, it's clear that standing out will be tough. Engagement with these similar products is low, with an average of only 3 comments per product launch. This suggests it's a challenge to capture user interest and that most product launches in this category are not memorable or successful. Since we don't have strong positive signals for 'use' or 'buy' from comparable products, it's difficult to gauge the demand. Given the mediocre success of similar solutions, be prepared to face significant headwinds and competition. Unless you offer something radically different or target an underserved niche, achieving profitability and recognition will be an uphill battle. Proceed with extreme caution and a clear understanding of the risks involved.

Recommendations

  1. Thoroughly investigate why existing real estate report generators and property organizers haven't achieved widespread adoption or significant user engagement. Identify the pain points they fail to address or the features they lack that could significantly improve user experience. This research will be crucial in differentiating your product and avoiding the pitfalls of your predecessors.
  2. Instead of competing directly with established players, focus on a specific niche within the real estate market that is currently underserved. For example, you could specialize in reports for commercial properties, vacation rentals, or sustainable homes. Tailoring your product to a specific audience will allow you to provide more relevant and valuable insights.
  3. Consider creating tools or features that can be integrated into existing real estate platforms or used by real estate agents and property managers. This approach allows you to leverage existing distribution channels and tap into a ready-made user base, potentially increasing your reach and adoption rate.
  4. Explore adjacent problems within the real estate industry that might be more promising or less saturated. For example, you could focus on developing AI-powered tools for property valuation, investment analysis, or tenant screening. This strategic pivot could open up new opportunities and position you as a leader in an emerging market.
  5. Given the competitive landscape and low engagement in this category, carefully evaluate whether this is the best use of your time and resources. It might be more prudent to explore other opportunities that offer a higher potential for success and a more favorable market environment. Consider cutting your losses now.
  6. Examine the criticisms of similar products, like issues with accuracy guardrails and premature trial expirations. These issues should inform your own development and launch strategy to ensure a smoother user experience.
  7. Based on user feedback from similar launches, incorporate features like the ability to generate Airbnb listing descriptions or provide ROI calculators for real estate investors. These additions could significantly enhance the value proposition of your product.

Questions

  1. What unique data sources or AI algorithms will your platform utilize to generate real estate reports that offer genuinely novel insights compared to existing solutions?
  2. How will you address concerns about the accuracy and reliability of AI-generated reports, particularly in the context of high-stakes real estate decisions?
  3. What specific marketing strategies will you employ to effectively reach your target audience and differentiate your product in a crowded market?

Your are here

You're entering a crowded space with your AI-powered real estate report generator and property organizer. The "Swamp" category isn't encouraging, and with 21 similar products already out there, it's clear that standing out will be tough. Engagement with these similar products is low, with an average of only 3 comments per product launch. This suggests it's a challenge to capture user interest and that most product launches in this category are not memorable or successful. Since we don't have strong positive signals for 'use' or 'buy' from comparable products, it's difficult to gauge the demand. Given the mediocre success of similar solutions, be prepared to face significant headwinds and competition. Unless you offer something radically different or target an underserved niche, achieving profitability and recognition will be an uphill battle. Proceed with extreme caution and a clear understanding of the risks involved.

Recommendations

  1. Thoroughly investigate why existing real estate report generators and property organizers haven't achieved widespread adoption or significant user engagement. Identify the pain points they fail to address or the features they lack that could significantly improve user experience. This research will be crucial in differentiating your product and avoiding the pitfalls of your predecessors.
  2. Instead of competing directly with established players, focus on a specific niche within the real estate market that is currently underserved. For example, you could specialize in reports for commercial properties, vacation rentals, or sustainable homes. Tailoring your product to a specific audience will allow you to provide more relevant and valuable insights.
  3. Consider creating tools or features that can be integrated into existing real estate platforms or used by real estate agents and property managers. This approach allows you to leverage existing distribution channels and tap into a ready-made user base, potentially increasing your reach and adoption rate.
  4. Explore adjacent problems within the real estate industry that might be more promising or less saturated. For example, you could focus on developing AI-powered tools for property valuation, investment analysis, or tenant screening. This strategic pivot could open up new opportunities and position you as a leader in an emerging market.
  5. Given the competitive landscape and low engagement in this category, carefully evaluate whether this is the best use of your time and resources. It might be more prudent to explore other opportunities that offer a higher potential for success and a more favorable market environment. Consider cutting your losses now.
  6. Examine the criticisms of similar products, like issues with accuracy guardrails and premature trial expirations. These issues should inform your own development and launch strategy to ensure a smoother user experience.
  7. Based on user feedback from similar launches, incorporate features like the ability to generate Airbnb listing descriptions or provide ROI calculators for real estate investors. These additions could significantly enhance the value proposition of your product.

Questions

  1. What unique data sources or AI algorithms will your platform utilize to generate real estate reports that offer genuinely novel insights compared to existing solutions?
  2. How will you address concerns about the accuracy and reliability of AI-generated reports, particularly in the context of high-stakes real estate decisions?
  3. What specific marketing strategies will you employ to effectively reach your target audience and differentiate your product in a crowded market?

  • Confidence: High
    • Number of similar products: 21
  • Engagement: Low
    • Average number of comments: 3
  • Net use signal: 3.5%
    • Positive use signal: 3.5%
    • Negative use signal: 0.0%
  • Net buy signal: 0.0%
    • Positive buy signal: 0.0%
    • Negative buy signal: 0.0%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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While LLMs like ChatGPT have revolutionized content creation, they still cannot create factual and visually-appealing data-intensive content that many industries such as real estate brokers, consultants or financial advisors need. With Dataherald AI (https://www.dataherald.com/sign-up), we made a technical preview of a tool that can automate the creation of data-intensive content for real estate professionals. Users can enter their queries and questions in a prompt-based interface about real estate market trends and the system returns factual text and accompanying data visualizations. The core engine works in three steps:1- Extract intent and entities from the user prompt (the metric they want, date ranges and location)2- Query structured data from various sources in our data warehouses3- Construct the final text and accompanying data visualization.In the first iterations we trained our model for the extraction step and used templated SQL with variables for entities in step 2. However with the release of GPT-4 and rapid development of LangChain we soon migrated to building on the LangChain framework and relying on GPT-4 to write SQL on the fly. From LangChain we use PromptTemplates, Memory, Example Selectors and Chains heavily. The three steps described above eventually became three chains we built and optimized.Overall we love Langchain. It allowed us to release quickly and leverage abstractions which cleaned up our code. One of the best parts about building on LangChain is the active community on Discord. However it is a sprawling framework and some components still do not seem ready for use in production. For example, we gave up on using SQLAgent pretty early on since it failed some easy testing.Some of the other challenges we faced: - GPT-4 is very slow and in our experience GPT3.5–turbo does not work well for building complex SQL queries on the fly. As a result the three chains currently take an average of 27s to produce an answer unless it is already cached.- When working with SQL results and structured datasets, you quickly hit the token limit when passing the SQL results to the LLM. We did not have access to the 32K context window, so once that is rolled out this should be less of a concern.- How to test? There does not seem to be many QA tools for LLM applications. We ended up building our own solutions to score generated content and flag low scores so we can add examples to our prompts.Looking forward to the next few weeks, we plan on delivering the following:1- Templates that allow users to create content that is optimized for blog posts and social media posts out of the box.2- Fix some limitations in prompts we can handle. You currently can’t compare across geo types (city with national trends).3- Add more datasets covering neighborhood level data.4- Add more chart and map types and viz customization featuresWould love to hear the community's thoughts. Are there tools or approaches that we should have considered? Does anyone use Langchain’s SQLAgent in production? Know any real estate professionals we should talk to?

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